Response predictor for pigment reduction after one session of photo-based therapy using convolutional neural network: A proof of concept study.

Background: Identifying treatment responders after a single session of photo-based procedure for hyperpigmentary disorders may be difficult.

Objectives: We aim to train a convolutional neural network (CNN) to test the hypothesis that there exist discernible features in pretreatment photographs for identifying favorable responses after photo-based treatments for facial hyperpigmentation and develop a clinically applicable algorithm to predict treatment outcome.

Methods: Two hundred and sixty-four sets of pretreatment photographs of subjects receiving photo-based treatment for esthetic enhancement were obtained using the VISIA® skin analysis system. Preprocessing was done by masking the facial features of the photographs. Each set of photographs consists of five types of images. Five independently trained CNNs based on the Resnet50 backbone were developed based on these images and the results of these CNNs were combined to obtain the final result.

Results: The developed CNN algorithm has a prediction accuracy approaching 78.5% with area under the receiver operating characteristic curve being 0.839.

Conclusion: The treatment efficacy of photo-based therapies on facial skin pigmentation can be predicted based on pretreatment images.